Model genetic rules based systems for evaluation of projects
The process of project evaluation is of vital importance for decision-making in organizations. In the particular case of IT projects, the historical average of successful projects is 30.7%, while renegotiated projects are 47.3% and cancelled projects are 22% [1]. These figures mean that huge budgets...
- Autores:
-
Silva, Jesus
Escobar Gomez, John Freddy
Steffens Sanabria, Ernesto
hernandez Palma, Hugo
Ikeda Tsukazan, Lucía Midori
Linares Weilg, Jorge Luis
Mercado, Nohora
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7800
- Acceso en línea:
- https://hdl.handle.net/11323/7800
https://doi.org/10.1016/j.procs.2020.03.069
https://repositorio.cuc.edu.co/
- Palabra clave:
- Genetic Algorithms
Gene Expression Programming
MCGEP Algorithm
Project Evaluation
Rules learning
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Model genetic rules based systems for evaluation of projects |
| title |
Model genetic rules based systems for evaluation of projects |
| spellingShingle |
Model genetic rules based systems for evaluation of projects Genetic Algorithms Gene Expression Programming MCGEP Algorithm Project Evaluation Rules learning |
| title_short |
Model genetic rules based systems for evaluation of projects |
| title_full |
Model genetic rules based systems for evaluation of projects |
| title_fullStr |
Model genetic rules based systems for evaluation of projects |
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Model genetic rules based systems for evaluation of projects |
| title_sort |
Model genetic rules based systems for evaluation of projects |
| dc.creator.fl_str_mv |
Silva, Jesus Escobar Gomez, John Freddy Steffens Sanabria, Ernesto hernandez Palma, Hugo Ikeda Tsukazan, Lucía Midori Linares Weilg, Jorge Luis Mercado, Nohora |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesus Escobar Gomez, John Freddy Steffens Sanabria, Ernesto hernandez Palma, Hugo Ikeda Tsukazan, Lucía Midori Linares Weilg, Jorge Luis Mercado, Nohora |
| dc.subject.spa.fl_str_mv |
Genetic Algorithms Gene Expression Programming MCGEP Algorithm Project Evaluation Rules learning |
| topic |
Genetic Algorithms Gene Expression Programming MCGEP Algorithm Project Evaluation Rules learning |
| description |
The process of project evaluation is of vital importance for decision-making in organizations. In the particular case of IT projects, the historical average of successful projects is 30.7%, while renegotiated projects are 47.3% and cancelled projects are 22% [1]. These figures mean that huge budgets are affected every year by errors in planning or control and monitoring of projects, with an economic and social impact. The objective of this research is to evaluate the MCGEP evolutionary algorithm in different versions databases with information on the evaluation of IT projects. The aim is to determine the possibility of applying an evolutionary algorithm that uses programming of genetic expressions as opposed to others of greater use. |
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2021 |
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2021-01-29T19:04:02Z |
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2021-01-29T19:04:02Z |
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2021 |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Text |
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https://doi.org/10.1016/j.procs.2020.03.069 |
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Corporación Universidad de la Costa |
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https://repositorio.cuc.edu.co/ |
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Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
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1 L. Thames, D. Schaefer Softwaredefined Cloud Manufacturing for Industry 4.0 Procedía CIRP, 52 (2016), pp. 12-17 2 Amelec Viloria, Dionicio Neira-Rodado, Omar Bonerge Pineda Lezama. Recovery of scientific data using Intelligent Distributed Data Warehouse. ANT/EDI40 2019: 1249-1254. 3 Schweidel D.A., Knox G. Incorporating direct marketing activity into latent attrition models Marke¬ting Science, 31 (3) (2013), pp. 471-487 4 Setnes M., Kaymak U. Fuzzy modeling of client preference from large data sets: an application to target selection in direct marketing Fuzzy Systems, IEEE Transactions on, 9 (1) (2001), pp. 153-163 5 Amelec Viloria, Omar Bonerge Pineda Lezama. Improvements for Determining the Number of Clusters in k-Means for Innovation Databases in SMEs. ANT/EDI40 2019: 1201-1206 6 Sosinsky B. Cloud Computing Bible, Wiley Publishing Inc., Indiana (2011), p. 3 7 Bravo M., Alvarado M. Similarity measures for substituting Web services International Journal of Web Services Research, 7 (3) (2010), pp. 1-29 8 Chen L., Zhang Y., Song Z.L., Miao Z. Automatic web services classification based on rough set theory Journal of Central South University, 20 (2013), pp. 2708-2714 9 Pineda Lezama O., Gómez Dorta R. Techniques of multivariate statistical analysis: An application for the Honduran banking sector Innovare: Journal of Science and Technology, 5 (2) (2017), pp. 61-75 10 Viloria A., Lis-Gutiérrez J.P., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). Tan Y., Shi Y., Tang Q. (Eds.), Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943, Springer, Cham (2018) 11 Nisa, R., Qamar, U.: A text mining-based approach for web service classification. Information Systems and e-Business Management, pp. 1–18 (2014). 12 Wu J., Chen L., Zheng Z., Lyu M.R., Wu Z. Clustering web services to facilitate service discovery Knowledge and information systems, 38 (1) (2014), pp. 207-229 13 Alderson J. A markerless motion capture technique for sport performance analysis and injury prevention: Toward a big data, machine learning future Journal of Science and Medicine in Sport, 19 (2015), p. e79 doi: 10.1016/j.jsams.2015.12.192. 14 Project Management Institute A Guide to the Project Management Body of Knowledge (6th Edition), Project Management Institute, Pennsylvania (2017) 15 Sean Marston, Zhi Li, Subhajyoti Bandyopadhyay, Juheng Zhang, Anand Ghalsas Cloud computing — The business perspective Decision support systems, Elsevier (2011), pp. 176-189 2010, Volume 51, Issue 1April 16 Bifet, A., & De Francisci Morales, G. (2014). Big data stream learning with Samoa. Retrieved from https://www.researchgate.net/publication/282303881_Big_data_stream_learning_with_SAMOA. 17 Mell Grance The NIST definition of cloud computing., NIST Special Publication (2011), pp. 800-845 18 Sitto K., M. Presser Field Guide to Hadoop, O’REILLY, California (2015), pp. 31-33 19 Alcalá R., Alcalá-Fdez J., Herrera F. A proposal for the genetic lateral tuning of linguistic fuzzy systems and its interaction with rule selection IEEE Transactions on Fuzzy Systems, 15 (4) (2007), pp. 616-635 20 Elsaid A., Salem R., Abdul-Kader H. A Dynamic Stakeholder Classification and Prioritization Based on Hybrid Rough-fuzzy Method Journal of Software Engineering, 11 (2017), pp. 143-159 21 Tan K.C., Yu Q., Ang J.H. A coevolutionary algorithm for rules discovery in data mining [Publicación periódica] // International Journal of Systems Science -, 37 (2006), p. 12 22 Bojarczuk C.C., Lopes H.S., Freitas A.A., Michalkiewicz E.L. A constrained-syntax genetic programming system for discovering classification rules: Application to medical data sets Artificial Intelligence in Medicine, 30 (1) (2004), pp. 27-48 ISSN 0933-3657. |
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Silva, JesusEscobar Gomez, John FreddySteffens Sanabria, Ernestohernandez Palma, HugoIkeda Tsukazan, Lucía MidoriLinares Weilg, Jorge LuisMercado, Nohora2021-01-29T19:04:02Z2021-01-29T19:04:02Z2021https://hdl.handle.net/11323/7800https://doi.org/10.1016/j.procs.2020.03.069Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The process of project evaluation is of vital importance for decision-making in organizations. In the particular case of IT projects, the historical average of successful projects is 30.7%, while renegotiated projects are 47.3% and cancelled projects are 22% [1]. These figures mean that huge budgets are affected every year by errors in planning or control and monitoring of projects, with an economic and social impact. The objective of this research is to evaluate the MCGEP evolutionary algorithm in different versions databases with information on the evaluation of IT projects. The aim is to determine the possibility of applying an evolutionary algorithm that uses programming of genetic expressions as opposed to others of greater use.Silva, JesusEscobar Gomez, John FreddySteffens Sanabria, Ernestohernandez Palma, HugoIkeda Tsukazan, Lucía Midori-will be generated-orcid-0000-0003-2466-7232-600Linares Weilg, Jorge Luis-will be generated-orcid-0000-0003-2570-4701-600Mercado, Nohoraapplication/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Procedia Computer Sciencehttps://www.sciencedirect.com/science/article/pii/S1877050920305068#!Genetic AlgorithmsGene Expression ProgrammingMCGEP AlgorithmProject EvaluationRules learningModel genetic rules based systems for evaluation of projectsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1 L. Thames, D. Schaefer Softwaredefined Cloud Manufacturing for Industry 4.0 Procedía CIRP, 52 (2016), pp. 12-172 Amelec Viloria, Dionicio Neira-Rodado, Omar Bonerge Pineda Lezama. Recovery of scientific data using Intelligent Distributed Data Warehouse. ANT/EDI40 2019: 1249-1254.3 Schweidel D.A., Knox G. Incorporating direct marketing activity into latent attrition models Marke¬ting Science, 31 (3) (2013), pp. 471-4874 Setnes M., Kaymak U. Fuzzy modeling of client preference from large data sets: an application to target selection in direct marketing Fuzzy Systems, IEEE Transactions on, 9 (1) (2001), pp. 153-1635 Amelec Viloria, Omar Bonerge Pineda Lezama. Improvements for Determining the Number of Clusters in k-Means for Innovation Databases in SMEs. ANT/EDI40 2019: 1201-12066 Sosinsky B. Cloud Computing Bible, Wiley Publishing Inc., Indiana (2011), p. 37 Bravo M., Alvarado M. Similarity measures for substituting Web services International Journal of Web Services Research, 7 (3) (2010), pp. 1-298 Chen L., Zhang Y., Song Z.L., Miao Z. Automatic web services classification based on rough set theory Journal of Central South University, 20 (2013), pp. 2708-27149 Pineda Lezama O., Gómez Dorta R. Techniques of multivariate statistical analysis: An application for the Honduran banking sector Innovare: Journal of Science and Technology, 5 (2) (2017), pp. 61-7510 Viloria A., Lis-Gutiérrez J.P., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). Tan Y., Shi Y., Tang Q. (Eds.), Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943, Springer, Cham (2018)11 Nisa, R., Qamar, U.: A text mining-based approach for web service classification. Information Systems and e-Business Management, pp. 1–18 (2014).12 Wu J., Chen L., Zheng Z., Lyu M.R., Wu Z. Clustering web services to facilitate service discovery Knowledge and information systems, 38 (1) (2014), pp. 207-22913 Alderson J. A markerless motion capture technique for sport performance analysis and injury prevention: Toward a big data, machine learning future Journal of Science and Medicine in Sport, 19 (2015), p. e79 doi: 10.1016/j.jsams.2015.12.192.14 Project Management Institute A Guide to the Project Management Body of Knowledge (6th Edition), Project Management Institute, Pennsylvania (2017)15 Sean Marston, Zhi Li, Subhajyoti Bandyopadhyay, Juheng Zhang, Anand Ghalsas Cloud computing — The business perspective Decision support systems, Elsevier (2011), pp. 176-189 2010, Volume 51, Issue 1April16 Bifet, A., & De Francisci Morales, G. (2014). Big data stream learning with Samoa. Retrieved from https://www.researchgate.net/publication/282303881_Big_data_stream_learning_with_SAMOA.17 Mell Grance The NIST definition of cloud computing., NIST Special Publication (2011), pp. 800-84518 Sitto K., M. Presser Field Guide to Hadoop, O’REILLY, California (2015), pp. 31-3319 Alcalá R., Alcalá-Fdez J., Herrera F. A proposal for the genetic lateral tuning of linguistic fuzzy systems and its interaction with rule selection IEEE Transactions on Fuzzy Systems, 15 (4) (2007), pp. 616-63520 Elsaid A., Salem R., Abdul-Kader H. A Dynamic Stakeholder Classification and Prioritization Based on Hybrid Rough-fuzzy Method Journal of Software Engineering, 11 (2017), pp. 143-15921 Tan K.C., Yu Q., Ang J.H. A coevolutionary algorithm for rules discovery in data mining [Publicación periódica] // International Journal of Systems Science -, 37 (2006), p. 1222 Bojarczuk C.C., Lopes H.S., Freitas A.A., Michalkiewicz E.L. A constrained-syntax genetic programming system for discovering classification rules: Application to medical data sets Artificial Intelligence in Medicine, 30 (1) (2004), pp. 27-48 ISSN 0933-3657.PublicationORIGINALModel genetic rules based systems for evaluation of projects.pdfModel genetic rules based systems for evaluation of projects.pdfapplication/pdf98714https://repositorio.cuc.edu.co/bitstreams/c4c8335a-4ca8-402d-97dc-068e86c0473b/downloadea50c3e4929caf5f398ecc0fcdc22d20MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/e32c32ee-d673-4498-bfec-4d435e30ccee/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/759b95a9-5293-4217-bdd9-9e4e680a0c7b/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILModel genetic rules based systems for evaluation of projects.pdf.jpgModel genetic rules based systems for evaluation of 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